Aiming at the mechanical equipment in the fault diagnosis process, the traditional Shannon–Nyquist sampling theorem is used for data collection, which faces main problems of storage, transmission, ...

https://journals.sagepub.com/doi/pdf/10.1177/0020294019898725

Compressed sensing reconstruction for axial piston pump bearing vibration signals based on adaptive sparse dictionary model

For the stability and maintenance of Smart Grids, a vast amount of power system data like synchrophasor data need to be collected and processed. Loss of power system data poses severe problems in operation and control of Smart Grids. Data loss may occur due to the transmission medium, congestion, sensor faults, etc. To tackle the data loss, a Compressive Sensing (CS) based data loss mitigation mechanism is proposed in this paper. In this mechanism, redundant data is transmitted to tackle the losses, and it consists of two techniques, feedback based technique and non-feedback technique. Feedback based technique primarily depends upon the data loss rate. In non-feedback based technique irrespective of data loss rate fixed redundancy is maintained. In the present work, an extensive simulated analysis is performed using synchrophasor data obtained from a Phasor Measurement Unit (PMU) for different data loss pattern (random, continuous) and rate (0 to 40%). It is observed that using the proposed mechanism; higher data loss rates are tackled with minimal degradation in reconstruction quality.

Data Loss Mitigation Mechanism using Compressive Sensing for Smart Grids

Bearing fault diagnosis is very important to improve the reliability and safety of axial piston pump There are some major problems that the existing techniques require large amount of storage, transmission and processing time for vibration signal In this paper, an adaptive compressive sensing method is developed for vibration signals reconstruction based on optimal classification algorithm Firstly, vibration signals were divided into blocks according to their cutting size and an energy sequence was produced in accordance with the energy of each signal block Secondly, the energy sequence was classified by the QPSO algorithm to obtain the biggest variance Whereafter, using the K-SVD algorithm, the proposed adaptive sparse dictionary learning framework enhances reconstruction capability of vibration signals Finally, bearing fault testing experiment validates the effectiveness of the adaptive compression sensing method method The proposed method has higher reconstruction performance than the classical K-SVD dictionary training method under the same compression ratio The reconstruction accuracy is further improved and the reconstruction time is shortened

Adaptive compressive sensing method based on optimal classification for bearing vibration signals in axial piston pump

Compressive sensing (CS) is a new framework for simulations sensing and compressive. How to reconstruct a sparse signal from limited measurements is the key problem in CS. For solving the reconstruction problem of a sparse signal, we proposed a self-adaptive proximal point algorithm (PPA). This algorithm can handle the sparse signal reconstruction by solving a substituted problem — l 1 problem. At last, the numerical results shows that the proposed method is more effective compared with the compressive sampling matching pursuit (CoSaMP).

A self-adaptive proximal point algorithm for signal reconstruction in compressive sensing

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A self-adaptive proximal point algorithm for signal reconstruction in compressive sensing